This course is intended for professionals that lack formal education in computer science, and that are in search of a simple and practical guide to algorithmic complexity. The course explains the concepts in layman's terms, and teaches how to reason about the complexity of algorithms without requiring one to have an extensive mathematical skillset. This course can also be handy for revising complexity concepts or Big-O analysis before interviews. Finally, the content also scratches the surface of some advanced analysis topics to provide a more encompassing image of the complexity theory.

Big-O Notation For Coding Interviews and Beyond

<p>The yardstick to measure the performance of algorithms is specified in terms of functions. For the mathematically uninitiated, we explain functions below.</p>

<h2>Functions</h2>
<p>Think of a function like a <i>machine</i> or a <i>blackbox</i> that takes inputs from one end and spits outputs from the other end.</p>

This lesson discusses the building blocks for analyzing algorithms.

Basics

Formal Analysis Tools

Recursive

Data-Structures

Amortized Analysis

Probabilistic Analysis

Complexity Theory

The End

Functions and Their Growth

Functions